Combining advanced mathematical modelling, data mining and new data sources to understand travellers' response to major disruptions (EPSRC DTP)


Contact Professor Stephane Hess ( to discuss this project further informally. Other supervisors will be Dr Chiara Calastri and Dr Charisma Choudhury.

Project description

Advanced mathematical models are widely used around the world to understand current travel behaviour and predict future demand. They are a key tool in helping shape policy and infrastructure decisions that often have major economic, environmental and societal impacts. The models are calibrated on data reflecting current travel patterns under 'stable' conditions.

There is little emphasis on their ability to predict how travellers will react to dealing with short term disruptions, ranging from major sports tournaments, temporary limits imposed on car travel during high air pollution periods, or major disruptions caused by weather events and terrorism.

Similarly, the arrival of new transport modes, such as electric or autonomous vehicles, or new services, such as shared vehicles, may lead to changes in behaviour that are difficult for models to predict. Traditional travel surveys are not able to capture the response to such 'shocks'. On the other hand, 'live' streams of data are continuously collected, ranging from quasi- transport data such as smartcard or mobile phone data, to non-numeric text based data on social media services.

These new data sources present a unique opportunity to understand responses to disruption. However, current modelling approaches need to be adopted to work with such data and combined with advanced data mining methods.

The aim of this PhD topic is threefold. The first step is to test how existing travel demand models perform in predicting the response to major disruptions.

The second step uses insights from mining alternative data sources to develop more robust models. Finally, the third step will combine traditional and new data sources during model development, benefiting from the way in which it can capture the response to short term disruptions.

Entry requirements

You must have achieved a bachelor degree with a 2:1 (hons) or equivalent, or a good performance in a Masters level course preferably in a quantitative discipline. We also recognise relevant industrial and academic experience.

Desired skills:

  • Strong numerical aptitude
  • Some experience in computer programming
  • Interest in choice and behavior modelling

If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.

How to apply

Formal applications for research degree study should be made online through the university's website.

If you require any further information, please contact the Graduate School Office e:, or t: +44 (0)113 343 35326.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.